Method and system for hybrid information query

ABSTRACT

Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.

BACKGROUND

1. Technical Field

The present teaching relates to methods, systems, and programming forInternet services. Particularly, the present teaching relates tomethods, systems, and programming for providing information to Internetusers.

2. Discussion of Technical Background

Search engines are programs that search documents for specified keywordsand return a list of the documents where the keywords were found. FIG. 1illustrates a prior art search engine 100. The search engine 100retrieves web pages by a web crawler. The contents of each page are thenanalyzed by a main module 102 to determine how it should be indexed.Data about web pages are stored in an index database 104 for use inlater queries. When a user 106 enters a query 108 into the search engine100 by using keywords, the main module 102 examines its index andprovides the user 106 a listing of best-matching documents, e.g., webpages, from the index database 104 as query results 110 according to itscriteria. However, the known search engine 100 only looks for the wordsor phrases in the documents exactly as entered from the query. It allowsonly query of documents through keywords. In other words, the query 108is limited to only keywords, and the query result is limited todocuments in the prior art search engine 100.

Personalized content recommendation may be available in the prior artsearch engine 100 by a content analyzer 112 and a content suggestionmodule 114. Traditional content recommendation may be realized in one oftwo ways—through collaborative filtering or content-based filtering.Collaborative filtering approaches build a model based on the user'spublic information, e.g., past behaviors, as well as similar decisionsmade by other users, and use that model to predict other content thatthe user may be interested in. Content-based filtering approachesutilize a series of discrete characteristics of known content stored inthe database 104 in order to recommend additional content with similarproperties. In addition, the prior art search engine 100 usuallyconsiders only explicit relationships among users and interests of usersexplicitly expressed based on their online content consumptionactivities. Implicit relationships, although handled in some existingtechnologies, are most identified via ad-hoc approaches. Furthermore,traditional recommendation systems usually only acquire data voluntarilyprovided by users, e.g., through questionnaires, or data recorded by therecommendation systems when users are directly interacting with therecommendation systems, e.g., cookies or activity logs when the usersare signing in the recommendation systems. As a result, new users orinactive users of the recommendation systems cannot be used to providedata for building recommendation models. Accordingly, for new users orinactive users whose personal data is unavailable or sparse, thetraditional systems become less effective in personalized contentrecommendation.

Therefore, there is a need to provide an improved solution for hybridinformation query based on information associated with users, whethersuch information is static, dynamic, offline, online, explicit orimplicit, all in a systematic and effective manner in order to solve theabove-mentioned problems.

SUMMARY

The present teaching relates to methods, systems, and programming forhybrid information query.

In one example, a method implemented on at least one machine, each ofwhich has at least one processor, storage, and a communication platformconnected to a network for hybrid information query is disclosed. Arequest is first received from a user associated with a hybrid query.The hybrid query is expressed in accordance with an input in terms ofone of a user, a feature, and a document, and a desired hybrid queryresult in terms of one of a user, a feature, and a document. A mappingis then determined between the input and the desired hybrid queryresult. A hybrid model is established based on hybrid informationcollected and associated with one or more users. The mapping isperformed based on the hybrid model to obtain the desired hybrid queryresult based on the input. Eventually, the desired hybrid query resultis provided as a response to the hybrid query.

In another example, a method implemented on at least one machine, eachof which has at least one processor, storage, and a communicationplatform connected to a network for hybrid information query isdisclosed. A request from a user associated with a hybrid query andauthorization information to access dynamic private information of theuser is first received. The hybrid query is expressed in accordance withan input in terms of one of a user, a feature, and a document, and adesired hybrid query result in terms of one of a user, a feature, and adocument. The dynamic private information associated with the user isthen received based on the authorization information. A hybrid modelestablished based on hybrid information collected and associated withone or more users is retrieved. The dynamic private information of theuser in the hybrid model is incorporated to obtain an updated hybridmodel. The desired hybrid query result is identified based on the inputand the updated hybrid model. Eventually, the desired hybrid queryresult is provided as a response to the hybrid query.

In still another example, a method implemented on at least one machine,each of which has at least one processor, storage, and a communicationplatform connected to a network for hybrid information query isdisclosed. Hybrid information related to one or more users iscontinuously collected. The hybrid information is continuously analyzedto identify one or more explicit relationships in the hybrid informationand to derive one or more implicit relationships among the one or moreusers based on the hybrid information. The hybrid information is thencontinuously indexed based on the explicit and implicit relationshipssuch that each of the one or more users is associated with at least oneof a topic of interest, another user, and content through hybridindices. A hybrid model is continuously updated based on the hybridindices. The hybrid model is used to derive a hybrid query result basedon a hybrid query. The hybrid query is expressed in accordance with aninput in terms of one of a user, a feature, and a document, and thehybrid query result in terms of one of a user, a feature, and adocument.

In a different example, a system for hybrid information query isdisclosed. The system includes a hybrid query interface and a hybridresponse recommendation engine. The hybrid query interface configured toreceive a request from a user associated with a hybrid query and providethe desired hybrid query result as a response to the hybrid query. Thehybrid query is expressed in accordance with an input in terms of one ofa user, a feature, and a document, and a desired hybrid query result interms of one of a user, a feature, and a document. The hybrid responserecommendation engine is configured to determine a mapping between theinput and the desired hybrid query result. The hybrid responserecommendation engine is further configured to retrieve a hybrid modelestablished based on hybrid information collected and associated withone or more users and perform the mapping based on the hybrid model toobtain the desired hybrid query result based on the input.

In another different example, a system for hybrid information query isdisclosed. The system includes a hybrid query interface, a hybridinformation fetcher, a hybrid modeling unit, and a hybrid responserecommendation engine. The hybrid query interface is configured toreceive a request from a user associated with a hybrid query andauthorization information to access dynamic private information of theuser and provide the desired hybrid query result as a response to thehybrid query. The hybrid query is expressed in accordance with an inputin terms of one of a user, a feature, and a document, and a desiredhybrid query result in terms of one of a user, a feature, and adocument. The hybrid information fetcher is configured to retrieve thedynamic private information associated with the user based on theauthorization information. The hybrid modeling unit is configure toretrieve a hybrid model established based on hybrid informationcollected and associated with one or more users and incorporate thedynamic private information of the user in the hybrid model to obtain anupdated hybrid model. The hybrid response recommendation engine isconfigured to identify the desired hybrid query result based on theinput and the updated hybrid model.

In still another different example, a system for hybrid informationquery is disclosed. The system includes a hybrid information fetcher, ahybrid information profiling unit, an explicit/implicitrelationship/interest indexer, and a hybrid modeling unit. The hybridinformation fetcher is configured to continuously collect hybridinformation related to one or more users. The hybrid informationprofiling unit is configured to continuously analyze the hybridinformation to identify one or more explicit relationships in the hybridinformation and to derive one or more implicit relationships among theone or more users based on the hybrid information. The explicit/implicitrelationship/interest indexer is configured to continuously index thehybrid information based on the explicit and implicit relationships suchthat each of the one or more users is associated with at least one of atopic of interest, another user, and content through hybrid indices. Thehybrid modeling unit is configured to continuously update a hybrid modelbased on the hybrid indices. The hybrid model is used to derive a hybridquery result based on a hybrid query. The hybrid query is expressed inaccordance with an input in terms of one of a user, a feature, and adocument, and the hybrid query result in terms of one of a user, afeature, and a document.

Other concepts relate to software for hybrid information query. Asoftware product, in accord with this concept, includes at least onemachine-readable non-transitory medium and information carried by themedium. The information carried by the medium may be executable programcode data regarding parameters in association with a request oroperational parameters, such as information related to a user, arequest, or a social group, etc.

In one example, a machine-readable tangible and non-transitory mediumhaving information for hybrid information query recorded thereon,wherein the information, when read by the machine, causes the machine toperform a series of steps. A request is first received from a userassociated with a hybrid query. The hybrid query is expressed inaccordance with an input in terms of one of a user, a feature, and adocument, and a desired hybrid query result in terms of one of a user, afeature, and a document. A mapping is then determined between the inputand the desired hybrid query result. A hybrid model is established basedon hybrid information collected and associated with one or more users.The mapping is performed based on the hybrid model to obtain the desiredhybrid query result based on the input. Eventually, the desired hybridquery result is provided as a response to the hybrid query.

In another example, a machine-readable tangible and non-transitorymedium having information for hybrid information query recorded thereon,wherein the information, when read by the machine, causes the machine toperform a series of steps. A request from a user associated with ahybrid query and authorization information to access dynamic privateinformation of the user is first received. The hybrid query is expressedin accordance with an input in terms of one of a user, a feature, and adocument, and a desired hybrid query result in terms of one of a user, afeature, and a document. The dynamic private information associated withthe user is then received based on the authorization information. Ahybrid model established based on hybrid information collected andassociated with one or more users is retrieved. The dynamic privateinformation of the user in the hybrid model is incorporated to obtain anupdated hybrid model. The desired hybrid query result is identifiedbased on the input and the updated hybrid model. Eventually, the desiredhybrid query result is provided as a response to the hybrid query.

In still another example, a machine-readable tangible and non-transitorymedium having information for hybrid information query recorded thereon,wherein the information, when read by the machine, causes the machine toperform a series of steps. Hybrid information related to one or moreusers is continuously collected. The hybrid information is continuouslyanalyzed to identify one or more explicit relationships in the hybridinformation and to derive one or more implicit relationships among theone or more users based on the hybrid information. The hybridinformation is then continuously indexed based on the explicit andimplicit relationships such that each of the one or more users isassociated with at least one of a topic of interest, another user, andcontent through hybrid indices. A hybrid model is continuously updatedbased on the hybrid indices. The hybrid model is used to derive a hybridquery result based on a hybrid query. The hybrid query is expressed inaccordance with an input in terms of one of a user, a feature, and adocument, and the hybrid query result in terms of one of a user, afeature, and a document.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe examples. The advantages of the present teachings may be realizedand attained by practice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems, and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 depicts a prior art search engine based on keywords query anddocuments query result;

FIG. 2 is high level exemplary system diagrams of a system for hybridinformation query, according to an embodiment of the present teaching;

FIGS. 3(a) and 3(b) illustrate a hybrid data space in which a system forhybrid information query operates, according to an embodiment of thepresent teaching;

FIG. 4 is a high level depiction of a self-contained system for hybridinformation query and its bootstrap capability, according to anembodiment of the present teaching;

FIG. 5 is a system diagram for an exemplary hybrid query engine of thesystem for hybrid information query, according to an embodiment of thepresent teaching;

FIG. 6 is a flowchart of an exemplary process of the hybrid queryengine, according to an embodiment of the present teaching;

FIG. 7 is a system diagram for an exemplary hybrid data archive of thehybrid query engine, according to an embodiment of the present teaching;

FIG. 8 is a system diagram for an exemplary hybrid data archive of thehybrid query engine, according to an embodiment of the present teaching;

FIG. 9 is a system diagram for an exemplary hybrid information fetcherof the hybrid query engine, according to an embodiment of the presentteaching;

FIG. 10 is a system diagram for an exemplary hybrid informationprofiling unit of the hybrid query engine, according to an embodiment ofthe present teaching;

FIG. 11(a) is a system diagram for an exemplary hybrid user informationanalyzer of the hybrid information profiling unit, according to anembodiment of the present teaching;

FIG. 11(b) is a system diagram for an exemplary hybrid contentinformation analyzer of the hybrid information profiling unit, accordingto an embodiment of the present teaching;

FIG. 11(c) is a system diagram for an exemplary hybrid activityinformation analyzer of the hybrid information profiling unit, accordingto an embodiment of the present teaching;

FIG. 12 is a system diagram for an exemplary hybrid modeling unit of thehybrid query engine, according to an embodiment of the present teaching;

FIG. 13(a) is a system diagram for an exemplary user informationcharacterization unit of the hybrid modeling unit, according to anembodiment of the present teaching;

FIG. 13(b) is a flowchart of an exemplary process of the userinformation characterization unit, according to an embodiment of thepresent teaching;

FIG. 14(a) is a system diagram for an exemplary user-related contentcharacterization unit and user activity characterization unit of thehybrid modeling unit, according to an embodiment of the presentteaching;

FIG. 14(d) is a flowchart of an exemplary process of the user-relatedcontent characterization unit and user activity characterization unit,according to an embodiment of the present teaching;

FIG. 15(a) is a system diagram for an exemplary modeling module of thehybrid modeling module, according to an embodiment of the presentteaching;

FIG. 15(b) is a system diagram for an exemplary model refinement unit ofthe modeling module, according to an embodiment of the present teaching;

FIG. 15(c) is a flowchart of an exemplary process of the modelingmodule, according to an embodiment of the present teaching;

FIG. 16 is a system diagram for an exemplary hybrid responserecommendation engine of the hybrid query engine, according to anembodiment of the present teaching;

FIG. 17 is a flowchart of an exemplary process of the hybrid responserecommendation engine, according to an embodiment of the presentteaching;

FIGS. 18(a) and 18(b) depict exemplary embodiments of a networkedenvironment in which hybrid information query is applied, according todifferent embodiments of the present teaching; and

FIG. 19 depicts a general computer architecture on which the presentteaching can be implemented.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present teachings.

The present disclosure describes method, system, and programming aspectsof hybrid information query. The method and system as disclosed hereinis capable of recommending any one of a user, a feature, and a documentas a response to a query given in any one of the forms of a user, afeature, and a document. The query and result in the present disclosureare associated not only based on explicit user interests orrelationships but also based on implicit relationships that are inferredand/or derived based on users' history of online behavior via, e.g.,derivation or propagation. Furthermore, the information query in thepresent disclosure is “hybrid” also in the sense that both online andoffline user and content information, such as static and dynamic userprofile and user-related content, are continuously collected to update amodel for response recommendation in a recurrent manner. Moreover, themethod and system are also self-adaptive based on users' activeness onthe system. The method and system have the ability to provide fairlygood recommendation as soon as a new user signs up to the system evenwith a few basic user attributes and then gradually and continuouslyimprove the profile of the user as the user's participation increases.

FIG. 2 is a high level illustration of a system for hybrid informationquery, according to an embodiment of the present teaching. The system200 includes a hybrid query engine 202 responsible for providing ahybrid query result 204 in response to a hybrid query 206 sent from auser 208. The system 200 is highly flexible as what can be queried andbased on what information. For example, the mapping relationshipsbetween the query and query result may be any one of user to user, userto document, user to feature, document to user, document to document,document to feature, feature to user, feature to document, and featureto feature. In one example, using users as a query, the hybrid queryengine 202 is able to recommend (1) another user, such as the user'sfriends, followers, interest group mates, etc. as explicitly presentedin the social networks, as well as “similar” users although notexplicitly connected, but algorithmically identified based on users'explicit relationships; (2) profile features of the user; and (3)documents the user explicitly created, liked, forwarded, commented,etc., and is likely interested according to a recommendation model. Inanother example, using documents as a query, the hybrid query engine 202is able to recommend (1) similar or related documents, where thesimilarity or relatedness is algorithmically calculated; (2) features ofthe documents, e.g., keywords or topics; and (3) users who explicitlycreate, like, forward, comment, etc., or is likely interested accordingto the recommendation model. In still another example, using features,such as keywords, entities, topics, latent variables, as a query, thehybrid query engine 202 is able to recommend (1) related features basedon their correlation with the query; (2) documents having the features;and (3) users whose profiles matching the features.

In this example, the hybrid query 206 and hybrid query result 204 arecommunicated through a hybrid query interface 210 as inputs and outputsof the hybrid query engine 202, and the recommendation is made by ahybrid response recommendation engine 212 in accordance with hybridrecommendation models 214 and hybrid information stored in a hybrid dataarchive 216. The hybrid information in the hybrid data archive 216 maybe continuously collected and updated by the hybrid query engine 202. Inaddition, in this example, personalized response recommendation may bemade based not only on the explicit relationship/interests but also onimplicit relationship/interests that can be inferred from hybridinformation collected from both offline and online user information,such as user behaviors. Both the explicit and implicitrelationship/interests are indexed by an explicit/implicitrelationship/interest indexer 218. The indexed relationships/interestsmay be stored in the hybrid data archive 216 as part of the hybridinformation and used for creating and continuously refining the hybridmodels 214.

FIGS. 3(a) and 3(b) illustrate a hybrid data space in which a system forhybrid information query operates, according to an embodiment of thepresent teaching. Users, content, activities, and features (e.g.,keywords) are all projected to the same space such that inference couldbe made based on implicit relationships among different data. FIG. 3(a)illustrates one example as to which information can be collected andutilized to infer hybrid relationships/interests of users or shared byusers. For example, shared interests among different users may beinferred based on user online activities. In one example, if the queryuser U1 clicks content C1, then the system can infer that U1 has animplicit interest in the topic of C1. In another example, if U1 forwardsC1 to another user Uk and Uk clicks C1, then the system can infer thatU1 and Uk share the same interest in the topic of C1. In still anotherexample, if U1 comments on C1 and another user Ui clicks U1's comment onC1, then the system can infer that U1 and Ui share the same implicitinterest in the topic of C1. In yet another example, if Ui clicksanother content Cn, and Uk also clicks the same content Cn, then thesystem can infer that Ui and Uk a shared the same interest in the topicof Cn. In yet another example, if U1 and U2 are in the same socialgroup, or if U1 follows or is followed by U2 and U1 clicks C1, then thesystem can infer that U2 is also interested in the topic of content 1.The hybrid relationships/interests of users or shared by users may beused for establishing mappings between query and query result. In oneexample, user's explicit or implicit interest in a particular topic ofcontent may be used to establish a link between the user and documentshaving the particular topic. In another example, the same interest in aparticular topic of content shared by two users may be used to establisha link between the two users.

FIG. 3(b) illustrates the hybrid data space in which a system for hybridinformation query operates. This hybrid space is different from theconventional schemes in that it utilizes a diverse range of activitiesto link users, documents, and features. From this space and theinter-relationships among different pieces of information, the systemcan group users into different interest groups, explore the relationshipbetween users, and identify implicitly hidden user relationships, aswell as their implicitly shared interests. In FIG. 3(b), in addition tousers, activities, and content, features are also part of the hybriddata space in which hybrid information query is performed. The featuresmay include, for example, keywords in a document, topics of a document,topics of an interest group, entities, or latent variables.

FIG. 4 is a high level depiction of a self-contained system for hybridinformation query and its bootstrap capability, according to anembodiment of the present teaching. In this example, when a new usersigns up to the system, the new user provides profile information, whichcan be used to leverage the existence of an existing hybrid model builtbased on existing users to make recommended responses. The existinghybrid model is built based on hybrid information including userattributes and documents. After the initial round of recommendation, thenew user's attributes can be incorporated into the existing model, andrefinement can be made based on further collected hybrid information onall users. Each new user continuously migrates to become an existinguser with their attributes being incorporated into the continuouslyrefined hybrid model. That is, the system may evolve like that andachieve self-improvement in the self-contained eco system.

FIG. 5 is a system diagram for an exemplary hybrid query engine of thesystem for hybrid information query, according to an embodiment of thepresent teaching. The hybrid query engine 202 includes a hybrid responserecommendation engine 212 configured to recommend any one of a user, afeature, and a document as a hybrid response to a hybrid query given inany one of the forms of user, feature, and document based on hybridinformation from the hybrid data archive 216 and hybrid models 214 builtand refined by a hybrid modeling unit 502.

As discussed before, the hybrid information includes any static,dynamic, online, or offline user information about the users and contentinformation associated with usage of such content by the users. Thehybrid information also includes activity information associated withonline activities of the users and all the explicit and implicitrelationships and interests of the users, which are indexed by theexplicit/implicit relationship interest indexer 218, in the forms ofindices. In addition, the hybrid information may further include deriveduser information and derived content information obtained based on theactivity information. In some embodiments, features information, such askeywords or topics of interest extracted from documents may be includedin the hybrid data archive 216 as part of the hybrid information.

In order to achieve up-to-date and comprehensive information profiling,the hybrid information may be obtained through both online and offlineinformation fetching mechanisms, i.e., continuously collection of staticinformation by a hybrid information fetcher 504 when users are offline,and real-time collection of dynamic information by the hybridinformation fetcher 504 in conjunction with a user sign in module 506when users are online. The hybrid information fetcher 504 is responsiblefor gathering extensive offline information, such as static user andcontent information that reflect a user's long-term interest or interestin generic contexts. The hybrid information fetcher 504 is alsoconfigured to, in conjunction with the user sign in module 506, captureonline user information, such as dynamic user and content informationduring the time when the users sign in to the system, which reflect auser's short-term interest or interest in a specific real-time context.The online and offline hybrid information have complementary purposes asthey reflect user's interests at complement resolutions (long-term vs.instant, generic vs. specific). As offline user information is morecomplete and more time-consuming to capture, the static user and contentinformation are continuously gathered in a recurrent manner. As todynamic user and content information, in one example, the user may signin and provide authorization information to the user sign in module 506,such as a private token with a time limit for accessing the user'ssocial network account, such that the hybrid information fetcher 504 isable to access and capture the user's private data from its privatesocial network account at a run-time. The private data may include theuser's most-recent news, events, blogs, social updates, etc. The fetchedinformation is “hybrid” also in the sense that the representation ofinformation can be very flexible: keywords based, user attributes, usercategories, latent space, and so on. The various representations can begenerally fitted into or derived from three categories of information asdiscussed above: user information about the users, content informationassociated with usage of such content by the users, and activityinformation associated with online activities of the users.

The “raw” data fetched by the hybrid information fetcher 504 may be fedinto a hybrid information profiling unit 508 to derive or inferadditional information in order to build online and offline profileswith respect to users or documents. The hybrid information profiling isdone by performing various analyses on the hybrid information, such asbut not limited to, extracting features from user attributes,deriving/inferring features from user attributes, deriving/inferringpreferences/interests from user attributes, extracting keywords fromdocuments, deriving/inferring topics of documents, predicting futureuser activity. The hybrid information profiling unit 508 is alsoresponsible for indexing explicitly related or implicitly related hybridinformation within the same category or across different informationcategories. As discussed above, in addition to explicit relationshipsand interests, implicit relationships and interests may be alsoidentified, for example, through analyzing user's activity informationalong with other hybrid information by the hybrid information profilingunit 508. The online and offline profiling and indexing may be eitheruser-centric or content-centric, i.e., building profiles around users ordocuments. The online and offline profiles and indices may be stored inthe hybrid data archive 216 as part of the hybrid information. All theindices that link two or more pieces of hybrid information may be storedin the relationship archive 510 for quick retrieval of a query resultonce a query input arrives.

The hybrid modeling unit 502 in this example is configured to build andcontinuously refine different recommendation models that map any type ofquery input to the same or different type of query result. In eachmodel, one type of hybrid information may be represented as a matrix,and two or more matrices may be applied to obtain a mapping from ahybrid query input to a hybrid query output. In one example, the modelmay be established based on a user attribute matrix representing userfeatures with respect to all existing users and a topic/keyword matrixrepresenting topics/keywords (i.e., content features) with respect tothe existing users.

It is noted that the model as described herein can be used to facilitateor enable such hybrid queries because the information contained in themodel allows comprehensive matching in order to identify the queryresult sought by a hybrid query. As discussed, user to user matchingbased on interested topics may be achieved by identifying rows intopic/keyword matrix that are similar. That is, users corresponding tosuch rows have shared interests. On the other hand, if one is queryabout other users who have similar attributes, user to user matching maybe performed by analyzing user attribute matrix on the similaritybetween the row representing the querying user and rows representingother users. In another example, if a querying user provides a documentas input and asks for a similar document, then a document to documentmatching need to be carried out. This can also be facilitated by themodel as disclosed herein so long as there are indices made from thetopics identified in topic/keyword matrix that point to the actualdocuments under such topics. In this case, the input document may befirst parsed and analyzed and features, e.g., keywords or topics, areidentified. Through such topics and by tracing by following the indices,similar documents can be identified. In the same fashion, if a documentis provided via a query and the request is to identify other users whoare interested in similar documents, rows in topic/keyword matrix thatinclude similar topics or keywords can be identified and userscorresponding to such rows can be identified as the query response.

In addition to attribute-topic matrices as discussed above, other matrixdata for building the hybrid models 214 may include user-user matricesof various relationships, user-content matrices of variousrelationships, user-attribute matrices, content-keywords matrices,keyword-keyword co-occurrence/co-location matrices, etc. To build thehybrid models 214, joint matrix factorization may be applied to some orall of the matrices with proper weights and regularization to obtainlatent representation of all users and contents, as well astransformation matrices mapping from user attributes to latent variablesand from keywords to latent variables.

In this example, the hybrid information fetcher 504, hybrid informationprofiling unit 508, and hybrid modeling unit 502 continuously runoffline in a recurrent manner to refresh the hybrid data archive 216,hybrid models 214, and relationship archive 510. Once a hybrid queryrequest is received by a hybrid query processing unit 512, for example,when a new user signs up or a third-party service provider sends arequest, the type of query input and desired query result is firstidentified. Depending on whether the query input and the desired queryresult is user, feature, or document, an appropriate processor in thehybrid query module 514 and an appropriate unit in the hybrid responsemodule 516 are selected by the hybrid response recommendation engine 212to make the recommendation by lookup table (indices) or similarityretrieval.

FIG. 6 is a flowchart of an exemplary process of the hybrid queryengine, according to an embodiment of the present teaching. Startingfrom blocks 602, 604, 606, user information, content information, andactivity information related to all users are fetched respectively andfed into the hybrid data archive 216. At block 608, hybrid profiling isperformed based on the continuously updated hybrid information retrievedfrom the hybrid data archive 216. Moving to block 610, hybrid models 214are built and continuously refined based on refreshed hybridinformation. At blocks 612, 614, 616, user based linking, feature basedlinking, and document based linking are also performed respectivelybased on the hybrid model 214 in a recurrent manner in order to indexthe recommended user, feature, and documents at block 618 for fastupdate and retrieval. It is noted that blocks 602-618 are all performedoffline to reduce latency during recommendation. Now moving to block620, once an existing user is detected to be online, a recommendationmay be made directly based on the current hybrid model and hybridinformation by following the corresponding indices. If the online useris a new user, the new user signs up at block 624 such that the systemmay set up an offline user profile for the new user at block 626 andperform hybrid profiling based on the new user's profile at block 628.

FIGS. 7 and 8 are system diagrams for an exemplary hybrid data archiveof the hybrid query engine, according to an embodiment of the presentteaching. As discussed above, each type of hybrid information may bestored in one of the nine sub-archives of the hybrid data archive 216.Sub-archives 702, 704, 706 store user information. For example, staticand dynamic user information may be collected by the hybrid informationfetcher 504 and stored in the static user-related information archive702 and dynamic user-related information archive 704, respectively. Thederived user-related information archive 706 stores user informationderived by the hybrid information profiling unit 508. In one example,link propagation methods may be applied to propagate user information(e.g., categories) of celebrities to every user in the social networkbased on the social graph and user activities (e.g., following,friends). The celebrity status of a social group member may be inferredby the number of followers in the social network. In general, theimportance of a user in a social network can be inferred and thepersonal interests of an importance user who have a significant numberof followers may be used to infer the interests of the followers.

Sub-archives 708, 710, 712 store content information. For example,static and dynamic content information may be collected by the hybridinformation fetcher 504 and stored in the static content-relatedinformation archive 708 and dynamic content-related information archive710. The derived content-related information archive 712 stores contentinformation derived by the hybrid information profiling unit 508. In oneexample, content with the same or similar topics may be derived fromstatic or dynamic content with a known topic. In another example,associated content, such as comments or edits on an article, may bederived from known static or dynamic content. In addition, activityinformation archive 714 stores activity information fetched by thehybrid information fetcher 504, and keyword information archive 716 andtopic information archive 718 store keywords and topics of interest,respectively, which are features derived from user information andcontent information by the hybrid information profiling unit 508.

As discuss above, cross-indexing can be applied to create indices withrespect to various hybrid information. For example, content-user indiceslink a document and a list of users who created, liked, shared, forward,commented the document, as well as the users likely interested in thedocuments determined by the hybrid models 214. In another example,content-feature indices (e.g., content-keyword, content-topic) link adocument and its extracted keywords and derived/inferred topics. Instill another example, each user may be linked to another user, afeature (keywords, topics), or content by user-based indices in the formof <key, value> pairs where the key is a user and the value is any oneof user, feature, and document. The indices in the hybrid data archive216 are continuously created with time stamps. Based on the hybridinformation and their indices, static and dynamic profiles 720, 724 maybe created based on either users or documents. For example, a staticprofile of a user reflects the user's generic attributes, such asgender, race, birthday, or long-term interests, while a dynamic profilereflects the user's short-term attributes, such as current location, orshort-term interests. The static and dynamic profiles 720, 724 may becombined to create integrated profiles 726 that reflect both long-termand short-term features.

As shown in FIG. 8, the indices may be created with labels by the staticindex builder 802, dynamic index build 804, and derived index builder806. The indexes may be built periodically in a recurrent manner.However, between any two runs, fold-in approaches may be applied to anynew documents or users to build incremental or delta indexes for themrespectively. Whenever a recommendation is needed, a lookup operation byfollowing proper indices with labels may be performed by the user crossindexed retriever 808, content cross indexed retriever 810, or theactivity cross indexed retriever 812 with minimum latency. The desiredquery result identified based on indices then can be retrieved from theproper sub-archive through a hybrid data access interface 814.

FIG. 9 is a system diagram for an exemplary hybrid information fetcherof the hybrid query engine, according to an embodiment of the presentteaching. Hybrid information may be dynamically monitored and retrievedby the hybrid information fetcher 504 from any content source 902through the network. The content sources 902 in this example include,but are not limited to, social network sites (e.g., Facebook, Renren,QQ), online news sources, online gaming sites, online shopping sites,blogs, micro-blogs (e.g., Twitter, Sina Weibo).

Different types of hybrid information may be monitored and gatheredthrough different mechanisms employed by the hybrid information fetcher504. In this example, the user-related content and user profile may becollected by a web crawler 904, and the user activities may be monitoredby an activity fetcher 906 through, e.g., a public API 908 which may beprovided by each content source 902. The user-related activities mayinclude any online activities that link users to content or reflect theuser's interests, such as browsing through a website, clicking anadvertisement, purchasing a product, following a business entity or anindividual (e.g., a friend), commenting, forwarding, liking or sharing ablog or micro-blog entry, updating status of a social network account,etc. A public information monitor 910 may be also employed by the hybridinformation fetcher 504 to gather any other suitable public informationthrough the public API 908. In addition, the hybrid information fetcher504 may include a user/content/activity filter/propagator 912, which maybe responsible for inferring the user's interests through, e.g., theuser's activities and social graph in a social network setting. Forexample, the user/content/activity filter/propagator 912 may beconfigured to apply link propagation methods to propagate labels (e.g.,categories) of celebrities to every user in the social network based onthe social graph and user activities (e.g., following, friends). Thecelebrity status of a social group member may be inferred by the numberof followers in the social network. In general, the importance of a userin a social network can be inferred and the personal interests of animportance user who have a significant number of followers may be usedto infer the interests of the followers. Also, as discussed above, aprivate information fetcher 914 may be employed to collect private datafrom content source 902, such as a user's social network account, uponuser authorization. In this example, user's social graph is collectedbased on public information gathered by the public information monitor910 and/or private data accessed by the private information fetcher 914.

In order to associate the monitored hybrid information to the correctuser, a user-based filter 916 may be employed by the hybrid informationfetcher 504 to, for each piece of information gathered, identify thecorrespondence between the information and the correct user. In thisexample, the user/content/activity filter/propagator 912 may be alsoresponsible for reducing the volume of content and activities that willbe saved to maximize the efficiency with minimal risks of losingimportant relevant information. Such reduction of volume in collecteddata may be performed according to certain criteria. In one example,outdated content or activities over a threshold time period may befiltered out. In another example, repetitive information may be removedby the user/content/activity filter/propagator 912. The hybridinformation fetcher 504 may also include a user profile analyzer 918configured to obtain user's sign-in information, such as attributesprovided by a new user, or attributes updated by an existing user. Asnoted above, the hybrid information fetcher 504 may be continuouslyrunning to provide and update dynamic user information and contentinformation and activity information to the hybrid data archive 216.

FIG. 10 is a system diagram for an exemplary hybrid informationprofiling unit of the hybrid query engine, according to an embodiment ofthe present teaching. The hybrid information profiling unit 508 includesa hybrid user information analyzer 1002, a hybrid content informationanalyzer 1004, and a hybrid activity information analyzer 1006. Thehybrid user information analyzer 1002 is configured to analyze userinformation, such as user attributes, and populate it to the userrelated sub-archives in the hybrid data archive 216. The hybrid contentinformation analyzer 1004 is configured to parse and analyze contentinformation, through for example, feature extraction, and populate theextracted features to the keywords and topic information archives in thehybrid data archive 216. The hybrid activity information analyzer 1006is configured to analyze the activities that link users to content,assign interests/relationship to users, and link them to content ofinterest and features (keywords/topics). The hybrid informationprofiling unit 508 may also include an offline profile generator 1008,an online profile generator 1010, and an integrated profile generator1012 for creating offline, online, and integrated profiles,respectively, based on hybrid information profiling, as discussed above.

For user information profiling or content information profiling, inaddition to keywords, topics, attributes, etc., there are also latentvariables. Latent variables may be derived from joint matrixfactorization, such as singular value decomposition, on user-usermatrices of all kinds of relationship, user-content matrices of allkinds of relationship, content-keywords matrices, etc. For example, userattributes in a user attribute matrix and topics/keywords in atopic/keyword matrix may be co-clustered into parallel topics/clusters.Matrix factorization then may be applied to both matrices with a sharedlatent user profile matrix, where each row corresponds to a latentprofile of a particular user, indicating the interests/kinships of theuser to each topic/cluster. Fold-in inference then may be applied toobtain a user latent vector based on either user attribute ortopic/keyword information.

FIG. 11(a) is a system diagram for an exemplary hybrid user informationanalyzer of the hybrid information profiling unit, according to anembodiment of the present teaching. The hybrid user information analyzer1002 includes a user-based indexer 1102, a static user informationanalyzer 1104, a dynamic user information analyzer 1106, and a deriveduser information generator 1108. The user-based indexer 1102 isresponsible for performing user based index for all user information.The static user information analyzer 1104 and dynamic user informationanalyzer 1106 are responsible for analyzing offline and online userinformation, respectively. User features, such as user attributes oruser preferences/topics of interest, may be obtained by the static anddynamic user information analyzer 1104, 1106 and indexed by theuser-based indexer 1102 with respect to other hybrid information in thehybrid data archive 216. In addition, based on the analyzed static ordynamic user information, relevant information on content, activities,or features may be retrieved by a hybrid information retriever 1110 fromthe hybrid data archive 216. The retrieved relevant information is thenused by an information inference unit 1112 in conjunction with thederived user information generator 1108 to infer, derive, or propagatethe known information based on predefined knowledge models 1114 toobtain derived user information. Such derived user information is thenstored back to the hybrid data archive 216 as related to the user andcan be useful to extract and store implicit or explicit relationships orinterests in the relationship archive 510. For example, the static userinformation analyzer 1104 by analyzing a user's offline user informationmay determine that the user has a long-term interest in basketball. Thedynamic user information analyzer 1106 may further determine that theuser just changed its current location to Chicago based on its mostrecent social network status update. The information inference unit 1112then may infer that the user is likely interested in basketball teams inChicago. Hybrid information related to basketball teams in Chicago maybe retrieved by the hybrid information retriever 1110 and cross-indexedwith respect to the user information. For example, articles regardingthe Chicago Bulls Team may be indexed with the user to form content-userindices, and players of the Chicago Bulls may be indexed with respect tothe user to form user-user indices. In one example, the informationinference unit 1112 may further identify that the user graduated fromNorthwestern University in Chicago and thus, derive that the user ismore likely interested in the college basketball team of NorthwesternUniversity instead of the Chicago Bulls. Such derived user informationis also cross-indexed with other hybrid information and stored back tothe hybrid data archive 216.

FIG. 11(b) is a system diagram for an exemplary hybrid contentinformation analyzer of the hybrid information profiling unit, accordingto an embodiment of the present teaching. The hybrid content informationanalyzer 1004 includes a content-feature cross indexer 1120, a staticcontent information analyzer 1122, a dynamic content informationanalyzer 1124, and a derived content information generator 1126. Thecontent-feature cross indexer 1120 is responsible for performingcontent-feature cross indexing. The static content information analyzer1122 and dynamic content information analyzer 1124 are responsible foranalyzing offline and online content information, respectively. Contentfeatures, such as keywords or topics, may be obtained by the static anddynamic content analyzer 1122, 1124 and indexed by the content-featurecross indexer 1120. In addition, based on the analyzed static or dynamiccontent information, relevant information on users (e.g., other userswho also reviewed the content just analyzed), activities (e.g., pastactivities of the same user or other users who share the same interestas the user at issue) or features may be retrieved from the hybrid dataarchive 216 to infer, derive, or propagate the known information by afeature extractor 1128 in conjunction with a feature archive/indexer1130 based on language models 1132 to obtain derived contentinformation, such as related content, content by the same author on thesame topic, content forwarded by others on the same topic, or contentretrieved from a link forwarded from another user sharing the sameinterest as the user at issue. Such derived content information is thenstored back to the hybrid data archive 216 as related to the user.

FIG. 11(c) is a system diagram for an exemplary hybrid activityinformation analyzer of the hybrid information profiling unit, accordingto an embodiment of the present teaching. The hybrid activityinformation analyzer 1006 includes an activity-based indexer 1140, anactivity information analyzer 1142, a dynamic activity informationanalyzer 1144, and a user/interest identifier 1146. The activity-basedindexer 1140 is responsible for performing activity-based indexing forall hybrid information. The activity information analyzer 1142 anddynamic activity information analyzer 1144 are responsible for analyzingpast and dynamic activity information, respectively. Based on theanalyzed past or dynamic activity information, relevant information onthe same user (e.g., reaction of other users who also reviewed thecontent just analyzed and known to share the same interest as the userat issue on the topic) or past activities of the same user or otherusers who also have the same profile as the current user may beretrieved by a hybrid information retriever 1148 from the hybrid dataarchive 216 to infer, derive, or predict the possible action that thecurrent user may have by an activity relevance assessment unit 1150based on behavior relevance criteria 1152. This predicted action may beused to compare with the actual action (collected later), and thediscrepancy may be used to refine the hybrid recommendation model. Suchpredicted activities are then stored back to the hybrid data archive 216as related to the user and can be useful to extract and store implicitor explicit relationships or interests in the relationship archive 510.As discussed above with respect to FIGS. 3(a) and 3(b), activityinformation along with other hybrid information, such as user or contentinformation, may be used to identify implicit user relationships orinterests by the user/interest identifier 1146.

FIG. 12 is a system diagram for an exemplary hybrid modeling unit of thehybrid query engine, according to an embodiment of the present teaching.The hybrid modeling unit 502 in this example includes a usercharacterization module 1202 and a modeling module 1204. The usercharacterization module 1202 in this example includes three units, eachof which is responsible for processing one type of hybrid information.The user-related content and user activities in the hybrid informationare characterized by the user-related content characterization unit 1208and user activity characterization unit 1210, respectively, and areconverted to topic/keyword information (e.g., represented by atopic/keyword matrix). The user profile (attributes) is characterizedand converted to user feature information (e.g., represented by a userattribute matrix) by the user information characterization unit 1206.Both the user feature information and topic/keyword information are fedinto the modeling module 1204 to generate hybrid recommendation models214. As noted above, the user profile and user-related content andactivities may be dynamically monitored and collected by hybridinformation fetcher 504. In addition, each new user, upon signing-up tothe system, may provide basic user profile through the user sign inmodule 506 (not shown here).

The modeling module 1204 is configured to establish hybrid models 214,such as a model that maps from users to topics of interest based on thetopic information and user feature information fed from the usercharacterization module 1202. In this example, the model may beestablished based on a user attribute matrix representing user featureswith respect to all existing users and a topic/keyword matrixrepresenting topics/keywords with respect to the existing users. It isnoted that the information to be fed to the modeling module 1204 may beselected in a manner to reduce the dimensionality of the user attributematrix or topic/keyword matrix in order to be computationallycompetitive. Such selected information may be the most relevant at thetime of the selection. Due to the fact that interests or context ofuser's environment may change over time, other collected data may bestill stored so that when needed, certain information can be retrievedand used when, e.g., the model needs to be drastically refined. Forexample, over time, a user's interest may change. This may be observedwhen recommended content has not been selected by the user. In thiscase, topics associated with such unselected content may be removed fromthe topic/keyword matrix and new interests may be retrieved to replacethe staled interests. It is understood that the dimension of the contentfeature matrix is usually reduced because of the large amount ofkeywords data. As to the user feature matrix, whether the dimensionreduction should be performed is a design choice made case by case.

FIG. 13(a) is a system diagram for an exemplary user informationcharacterization unit of the user characterization module, according toan embodiment of the present teaching. The user informationcharacterization unit 1206 may include a user attribute analysis unit1302, a user attribute categorization unit 1304, and a user attributequantification unit 1306. The user attribute analysis unit 1302 isresponsible for extracting basic user attributes from received userprofiles. The features extracted by the user attribute analysis unit1302 may be represented as a feature vector for each user, and all thefeature vectors may be directly saved in the feature database 1308without any dimension reduction process. In other words, the featuredatabase 1308 in this example stores feature vectors in their originaldimensions. The user information characterization unit 1206 generatesuser feature information, which may be represented as an m×n matrix,with rows corresponding to users and columns corresponding to userfeatures in a reduced dimension. To reduce computational complexity, thedimension of user feature vectors in the matrix may be reduced by theuser attribute categorization unit 1304 and user attributequantification unit 1306 compared with the original dimension of featurevectors stored in the feature database 1308.

The user attribute categorization unit 1304 is configured to derivecategorical features for each user base on correlation of its valueswith content interests, i.e., predefined feature categorizationconfiguration 1310. In one example, for “city” attribute, features suchas whether it is from first-tier cities such as Beijing, Shanghai, theInternet penetration rate of that city, etc., may be derived by the userattribute categorization unit 1304. In another example, for“university/department” attribute, derived features may include whetherthe department is technology, art, or science, the tier of theuniversity, etc. The user attribute quantification unit 1306 isconfigured to quantify each attribute into value ranges according todata analysis, i.e., predefined feature quantification configuration1312. In one example, for “birthday” attribute, it may be quantified bythe user attribute quantification unit 1306 into predefined age groups,such as 20+, 40+, 60+, etc. Also, “birthday” attribute may be quantifiedto derive two other features: constellation and Chinese Zodiac. Based onastrology and numerology, features such as a user's personality may beinterred based on his/her constellation and Chinese Zodiac, which may befurther combined with other features to infer the user's possible socialroles and topics of interest. In still another example, statisticaldata-driven approaches may be applied to feature quantification. Forexample, topics may be considered as labels and attributes may beconsidered in their real values as features. The effective featurequantification then may be obtained from the data according to thecut-off values of each attribute in a decision tree classifier.Eventually, the user feature vectors in a reduced dimension areoutputted. As noted above, the user feature vectors include featuresextracted from users' offline information, which reflect users'long-term and generic interest, features representing users' attributes(e.g., gender, age), which are especially useful for inactive users, andfeatures extracted from online user information at a run-time per usersigns-in, which reflect users' short-term interest.

FIG. 13(b) is a flowchart of an exemplary process of the userinformation characterization unit, according to an embodiment of thepresent teaching. Starting from block 1320, hybrid user information,whether it is online or offline information, is received by the userinformation characterization unit 1206. The user information is thenanalyzed at block 1322 by the user attribute analysis unit 1302 toextract original user profiles. The original user profiles may bearchived in the feature database 1308 at block 1324 for future use, suchas model refinement. Proceeding to block 1326, user features may becategorized to derive features based on predefined featurecategorization configuration. At block 1328, categorical user featuresmay be further quantified with respect to predefined featurequantification configuration to reduce the dimension of the user featurevectors. Eventually, at block 1330, user feature information with areduced dimension is generated by the user information characterizationunit 1206.

FIG. 14(a) is a system diagram for an exemplary user-related contentcharacterization unit and user activity characterization unit of theuser characterization module, according to an embodiment of the presentteaching. The user-related content characterization unit 1208 and useractivity characterization unit 1210 are responsible for generatingtopic/keyword information that indicates each user's interest profile.The topic/keyword information may be represented as an m×h topic/keywordmatrix, with rows corresponding to users and columns corresponding totopics/keywords in a reduced dimension.

The user-related content characterization unit 1208 may include a usercontent analyzer 1402 responsible for performing keywords selection fromthe hybrid content information based on language models 1404 andvocabulary 1406 and storing the extracted keywords in a user keywordsstorage 1408. As discussed above, the content information may be anyonline or offline content consumed or contributed by the user, such asnews, articles, events, blogs, social updates, etc. The user contentanalyzer 1402 may apply any known language models to extract keywordsand/or identify topics of interest from the content, e.g., by featureselection methods in text classification, such as document frequency(how many documents in the corpus a word occurs in), mutual information,information gain, chi-square, etc. All those feature selection methodsmay help selecting of the most indicative keywords or key phrases fromvarious candidate keywords with respect to any predefined category(topic of interest) from the hybrid content information.

The user activity characterization unit 1210 may include a user activityanalyzer 1410 responsible for analyzing the user's activity informationbased on activity context information and topic hierarchy 1412.Optionally, all the collected user activities may be stored in anactivity storage 1414 for future use. The activity context informationindicates the context of each user activity, such as the time when theactivity occurs, the site where the activity happens, etc., which mayhave different weights when different user activities are aggregated.Additionally, an user action, such as “click,” is conventionallyconsidered as a typical activity. In this example, “forward” is also anactivity that can be observed and used to infer interests. Activitieslink content to an action and such activities reflect the interest of auser to the content. Different activities may weight that interestdifferently. For example, if a user clicks on a document, it may reflectthe fact that the user likes the content. If the activity is “forward,”this activity weighs more, i.e., there is a stronger degree of likeassociated with the user, e.g., “like it very much.” Furthermore, if theuser even commented on the content (another activity), it may indicatethat the user likes the content a lot because the user activelyparticipated in contributing to the peripheral of the content.

The user activity characterization unit 1210 may also include atopic/keyword determiner 1416 configured to determine topics of interestbased on the user activities, the extracted keywords from the userkeywords storage 1408, and the topic hierarchy 1412. In one example, theactivities and keywords may be classified under predefined topics interms of the same taxonomy in the topic hierarchy 1412 by any knownclassifier. For example, activities and keywords related to the sameuser may be aggregated through weighted linear combination into a singletopic vector. In addition to explicit interests, as noted above, topicsof interest for each user may be also inferred as implicit interests bytopic propagation methods in a social network setting. The determinedtopics of interest may be represented as a real-value vector (i.e., avector of weights with respect to keywords and topics) for each user andstored in a topic/keyword database 1418 in their original dimensions. Inorder to reduce the dimension of topics/keywords in the topic/keywordmatrix, a topic/keyword dimension reduction unit 1420 may be applied inconjunction with predefined reduction aggressiveness configuration 1422.In one example, known feature selection methods in text classificationmay be applied to calculate scores for each <topic, keyword> pair. Thescores are then used to rank all the keywords for each topic. By settinga threshold on the scores or the number of keywords selected for eachtopic, the dimension of the topic vectors may be reduced. For example,keywords such as “football,” “basketball,” “Michael Jordan,” or “NBA,”may be considered as the most indicative keywords for “sports” topic andthus, are included in the topic/keyword matrix.

FIG. 14(b) is a flowchart of an exemplary process of the user-relatedcontent characterization unit and user activity characterization unit,according to an embodiment of the present teaching. At block 1430,hybrid content information is received by the user-related contentcharacterization unit 1208. At block 1432, the received content isanalyzed to extract keywords based on language models and vocabulary.Topics of interest related to the received hybrid content informationare then estimated at block 1434 by, for example, statisticalclassifiers. At block 1436, user activity information is also receivedby the user activity characterization unit 1210. The received activitiesare analyzed at block 1438, and their natures, such as whether anactivity supports or negates a topic, are determined at block 1440.Implicit topics of interest (e.g., supporting or negating a topic) maybe identified at block 1442 based on the estimated topics and determinednatures of activities. Proceeding to block 1444, all the identifiedtopics associated with each user, whether explicit or implicit, may bearchived in the topic/keyword database 1418. At block 1446,dimensionality reduction may be performed to reduce the dimension oftopics in the topic/keyword matrix. Eventually, at block 1448, topicinformation with a reduced dimension is generated by the user-relatedcontent characterization unit 1208 and user activity characterizationunit 1210.

FIG. 15(a) is a system diagram for an exemplary modeling module,according to an embodiment of the present teaching. In this example, themodeling module 1204 includes an initial modeling unit 1502, a modelintegration unit 1504, and a model refinement unit 1506. The initialmodeling unit 1502 is configured to provide an initial model to a hybridresponse recommendation module 1508 based on, for example, a userattribute matrix and a topic/keyword matrix of the existing users. Inthis example, the modeling module 1204 further includes a user attributematrix generator 1510 and a topic/keyword matrix generator 1512. Asnoted above, the user characterization module 1202 may be responsiblefor providing the user attribute matrix and topic/keyword matrix in theuser feature information and topic/keyword information, respectively. Inthis example, a dimension reduction unit 1514 may be employed by themodeling module 1204 to reduce dimensions of the feature vectors andtopic vectors that are stored in a user feature database 1516 and atopic/keyword database 1518, respectively, in their original dimensions.The user attribute matrix generator 1510 and topic/keyword matrixgenerator 1512 then combine user feature vectors and topic/keywordvectors for all existing users to generate the user attribute matrix andtopic/keyword matrix, respectively.

The model integration unit 1504 is configured to generate an integratedmodel by continuously appending the information of each new user (e.g.,new user profile, estimated topics of interest) to the user attributeand topic/keywords matrices of the existing model. Given that onlineactivities continuously occur and change, the model refinement unit 1506is responsible for dynamically refining the hybrid recommendation models214 based on dynamic user-related content and activities andcharacterized user features and topics of interest. In addition, thediscrepancy between the estimated topics and the actual user selectedcontent may be used by the model refinement unit 1506 to adjust thecurrent recommendation model. It is noted that whether a user selects ornot selects a piece of suggested content is part of the dynamic behaviorof the user or user's activity. The up-to-date hybrid model may bealways provided to the topic estimation module 412 for topicsstimulation.

In addition to the above-mentioned user attribute matrix andtopic/keyword matrix, latent variables may be derived by joint matrixfactorization, such as singular value decomposition, from variousmatrices, including user-user matrices of all kinds of relationships,user-document matrices of all kinds of relationships, document-keywordmatrix, user-keyword matrix (extracted from user self-tags andexplicitly subscribed keywords, for example), etc.

Through the user attribute matrix and topic/keyword matrix, variousrelationships can be derived. For example, through joint matrixfactorization, user to user relationships may be inferred by identifyingusers who have similar attributes and interests by identifying rows inboth matrices that are similar. For example, similar rows in the userattribute matrix correspond to users who have similar attributes.Similar rows in the topic/keyword matrix correspond to users who sharesimilar interests. Users who are similar in both matrices may be thosewho are similar in attributes and share common interests. Via thosematrices, one can identify both users who are implicitly related, e.g.,who, although never met and never communicated, are nevertheless relatedvia common interests. Based on such matrices, a user-user matrix may beconstructed reflecting the relationship between users, either explicitor implicit. For instance, for any user, a matrix may be built with rowscorresponding to other users considered to be related to the user. Thoseidentified related users can be those to whom the user sends or forwardsarticles, documents or any other information in certain topics tofriends. Those friends are clearly or explicitly related to the user.Inversely, the identified users as being related to the user can bethose who send or share content to the user. Those are users who areexplicitly related and can be identified through monitored user'sactivities. At the same time, by identifying similar rows in the userattribute matrix, users, possibly not known to each other, may beidentified as implicitly related by having similar attributes. Inaddition, by detecting similar rows in the topic/keyword matrix, users,possibly not known to each other at all, who share common interest maybe identified. Such users who do not know each other yet identified assimilar are implicitly related and can be added to the user-user matrixto record all possible, explicit or implicit, related users. In thismatrix, for each pair of relationship, e.g., say between a first user asecond user, it may be marked as to how they are related. For example,the columns of the user-user matrix may correspond to attribute,different topics/keywords, or social connections such as via differentsocial groups. Through those means, people who are either explicitly orimplicitly related to a particular user may be identified, and suchinformation can be consolidated in a user-user matrix that will enableany types of user to user query.

Similarly, one can also explicitly construct user-document matrices. Forexample, for each user, any document that the user explicitly accessesmay be included in such a matrix. In addition, documents that can beinferred via implicit relationship may also be included. For example, ifthere are other users who are considered to be related to the user,e.g., on certain shared interests, any documents accessed by those otherusers, may also be included in the user-document matrix, with, e.g., anindication as to the source of the document and how it is related. Foreach of such document, some characterization, e.g., a topic, may berecorded to facilitate user to document recommendation. This matrix isconstantly adjusted. Documents that have not been accessed for a whileor explicitly rejected by the user may be removed. New documents can beadded based on monitored online activities. In this way, the recommendedcontent to the user can be kept fresh and updated.

Furthermore, additional matrices may also be constructed to organize thevast amount of information collected or observed. For example, adocument-keyword matrix may be constructed in which for each document,various keywords may be extracted based on the importance of the wordsin the document, as discussed before. This can be done using any knowntechnologies. Each document in such a matrix may have cross index to,say, the document cited in the user-document matrix. Similarly, eachuser in the user-document matrix may be cross-indexed to the userslisted in the user-user matrix. In this manner, information in eachmatrix is targeted for certain mapping but through the cross indicesacross different matrices, complex relationships and additionalinformation can be mined and derived.

FIG. 15(b) is a system diagram for an exemplary model refinement unit ofthe modeling module, according to an embodiment of the present teaching.In this example, the model refinement unit 1506 includes a discrepancydetector 1520, a refinement mode determiner 1522, a user subgroup filter1524, a time-based interest filter 1526, a location-based interestfilter 1528, a matrix update unit 1530, and a model refiner 1532. Asnoted above, discrepancy between the estimated topics and the actualuser selected topics may be detected and analyzed by the discrepancydetector 1520. Depending on the degree of discrepancy, the refinementmode determiner 1522 is responsible for deciding a mode in which themodel refinement will be conducted. In one example, if the discrepancyis below a threshold value, a gradual refinement mode may be selected.The matrix update unit 1530 then updates the user attribute matrix andtopic matrix based on the dynamically monitored user information. Inanother example, if the discrepancy is above a threshold value, whichmeans the estimated topics of interest are not what the user expects, atopic/keyword matrix remapping unit 1534 may be employed to update thetopic/keyword matrix by swapping the estimated topics out of thetopic/keyword matrix.

In still another example, hierarchical models with sub-models may beapplied to further refine the hybrid recommendation model 214. Forexample, hierarchical models based on different time frames or locationsmay be applied by the time-based interest filter 1526 and location-basedinterest filter 1528 to collect only user-related content and activitiesthat fit into a specific time frame or location for each user. Forexample, a user on week days and weekend/evening times may havedifferent interested topics when online. Sub-models for each user may bedivided into such time frames and used accordingly depending on the timeat which a recommendation needs to be made. Similarly, sub-models basedon user subgroups may be applied by the user subgroup filter 1524 tocollect only content and activities of users in a specific subgroup.More similar users may be grouped together to more precisely model theinterests of this subgroup. The hierarchical models applied by the usersubgroup filter 1524, time-based interest filter 1526, or location-basedinterest filter 1528 may be fed into the matrix update unit 1530 tocause the model refiner 1532 to adjust the hybrid recommendation models214. For example, the initial model may be divided into sub-models withrespect to time, location or user, as noted above. The model refiner1532 may further generate sets of sub-matrices for the sub-models andestablish recommendation models using the new sub-matrices.

FIG. 15(c) is a flowchart of an exemplary process of the modelingmodule, according to an embodiment of the present teaching. Startingfrom block 1540, user feature information and topic information arereceived by the modeling module 1204. At block 1542, a user attributematrix and a topic/keyword matrix are generated based on the receiveduser feature information and topic/keyword information. A recommendationmodel is then established at block 1544 using the user attribute matrixand topic/keyword matrix. Proceeding to block 1546, dynamic user onlinebehavior information such as dynamic user-related content and useractivities are continuously monitored and received by the modelrefinement unit 1506. At block 1548, the mode for refining therecommendation model is determined. If a gradual refinement mode ischosen, at block 1550, the user attribute matrix and topic/keywordmatrix are updated by the model refinement unit 1506 using thedynamically updated user information. If estimated topics of interestprovided by the current recommendation model are deemed to be undesiredat block 1552, the topic/keyword matrix may be updated using thenext-best topics at block 1554. Otherwise, a hierarchical model may beapplied to adjust the current recommendation model. At block 1556, thecurrent recommendation model may be divided into sub-models with respectto time, location or user. New sets of user attribute sub-matrices andtopic sub-matrices may be generated at block 1558 for the dividedsub-models. Eventually, at block 1560, a refined recommendation modelmay be established using the new sets of user attribute sub-matrices andtopic sub-matrices.

FIG. 16 is a system diagram for an exemplary hybrid responserecommendation engine of the hybrid query engine, according to anembodiment of the present teaching. As discussed above, a nine-wayquery-result matching may be performed by a corresponding recommendationunit in the hybrid response recommendation engine 212 based on thehybrid recommendation model 214. In this example, each recommendationunit may perform a lookup operation by following proper indices. In oneexample, if a user-based query is requested to search for other relevantusers, the user-user recommendation unit may perform a lookup operationbased on the indices with respect to the user in the query input, inparticular, user-user indices. As discussed above, each index may have alabeled on it, which facilitates the lookup operation during search andrecommendation. For user-user indices, the label may be for example,“sharing an explicit or implicit common interest,” “in the same socialgroup,” etc. In another example, if a document-based query is requestedto search for users, the document-user recommendation unit may perform alookup operation by following proper document-user indices with labelssuch as “contributing to the same document,” or “likely interested inthe topic of the document,” etc. The index-based operation may greatlyreduce latency for recommendation as all the indexing operations arecontinuously performed offline, and the indices are stored for quickretrieval. To balance latency and accuracy, it is noted that onlinehybrid information including online user information, contentinformation, and activity information, which are captured from theuser's private data when the user is online, may be utilized toreal-time adjust the query result obtained based on the indices. Inother words, real-time, specific context information may influence thefinal query result provided to the user. In one example, the user-userrecommendation unit may identify several relevant users as possiblequery results for a user to user query based on user-user indices.However, the online user activity information indicates that the userjust stopped following one of the relevant users in its social network.In this case, the hybrid response recommendation engine 212 may adjustthe query result by removing that user in real-time.

FIG. 17 is a flowchart of an exemplary process of the hybrid responserecommendation engine, according to an embodiment of the presentteaching. Starting from block 1702, a request associated with a hybridquery is received by the hybrid response recommendation engine 212. Thehybrid query is expressed in accordance with an input in terms of one ofa user, a feature, and a document, and a desired hybrid query result interms of one of a user, a feature, and a document. At block 1704, amapping between the input and the desired hybrid query result isdetermined. A hybrid model established based on hybrid informationcollected and associated with one or more users is then retrieved atblock 1706. Proceeding to block 1708, the mapping is performed based onthe hybrid model to obtain the desired hybrid query result based on theinput. Eventually, at block 1710, the desired hybrid query result isprovided as a response to the hybrid query.

FIGS. 18(a) and 18(b) depict high level exemplary system configurationsin which hybrid information query is performed, according to differentembodiments of the present teaching. In FIG. 18(a), the exemplary system1800 includes a hybrid query engine 1802, a content portal 1804, users1806, a network 1808, and content sources 1810. The network 1808 may bea single network or a combination of different networks. For example,the network 1808 may be a local area network (LAN), a wide area network(WAN), a public network, a private network, a proprietary network, aPublic Telephone Switched Network (PSTN), the Internet, a wirelessnetwork, a virtual network, or any combination thereof. The network 1808may also include various network access points, e.g., wired or wirelessaccess points such as base stations or Internet exchange points 1808-a,. . . , 1808-b, through which a data source may connect to the networkin order to transmit information via the network.

Users 1806 may be of different types such as users connected to thenetwork 1808 via desktop connections (1806-d), users connecting to thenetwork 1808 via wireless connections such as through a laptop (1806-c),a handheld device (1806-a), or a built-in device in a motor vehicle(1806-b). A user 1806 may send a request associated with a hybridinformation query and user information to the content portal 1804 (e.g.,a search engine, a social network site, etc.) via the network 1808 andreceive recommended hybrid response from the content portal 104 throughthe network 1808. The hybrid query engine 1802 in this example may workas backend support to provide a desired hybrid query result to thecontent portal 1804 based on the hybrid query in the request from theuser 1806. That is, the content portal 1804 in this example may be anentity that uses the hybrid query engine 1802 as a vendor to process ahybrid information query in the user request. The request from thecontent portal 1804 includes the hybrid query from the user 1806 whilethe result from the hybrid query engine 1802 passes on the output to thecontent portal 1804.

The content sources 1810 include multiple content sources 1810-a,1810-b, . . . , 1810-c. A content source may correspond to a web sitehosted by an entity, whether an individual, a business, or anorganization such as USPTO.gov, a content provider such as cnn.com andYahoo.com, a social network website such as Facebook.com, or a contentfeed source such as tweeter or blogs. Both the hybrid query engine 1802and content portal 1804 may access information from any of the contentsources 1810-a, 1810-b, . . . , 1810-c to obtain dynamic hybridinformation related to the users 1806. For example, the hybrid queryengine 1802 may monitor and gather dynamic user-related content andactivities and user profile from the content sources 1810 and useinformation as the basis for continuously updating the recommendationmodel for providing up-to-date hybrid query results.

FIG. 18(b) presents a similarly system configuration as what is shown inFIG. 18(a) except that the hybrid query engine 1802 is now configured asan independent service provider that interacts with the users 1806directly to provide hybrid information query service. In the exemplarysystem 1812, the hybrid information recommendation 102 may receive arequest with basic information from a user 1806 and/or dynamic contentassociated with user 1806 and provide desired hybrid query results tothe user 1806 directly without going through a third-party contentportal 1804.

To implement the present teaching, computer hardware platforms may beused as the hardware platform(s) for one or more of the elementsdescribed herein. The hardware elements, operating systems, andprogramming languages of such computers are conventional in nature, andit is presumed that those skilled in the art are adequately familiartherewith to adapt those technologies to implement the DCP processingessentially as described herein. A computer with user interface elementsmay be used to implement a personal computer (PC) or other type of workstation or terminal device, although a computer may also act as a serverif appropriately programmed. It is believed that those skilled in theart are familiar with the structure, programming, and general operationof such computer equipment and as a result the drawings should beself-explanatory.

FIG. 19 depicts a general computer architecture on which the presentteaching can be implemented and has a functional block diagramillustration of a computer hardware platform that includes userinterface elements. The computer may be a general-purpose computer or aspecial purpose computer. This computer 1900 can be used to implementany components of the hybrid information query architecture as describedherein. Different components of the system 200 can all be implemented onone or more computers such as computer 1900, via its hardware, softwareprogram, firmware, or a combination thereof. Although only one suchcomputer is shown, for convenience, the computer functions relating tohybrid information query may be implemented in a distributed fashion ona number of similar platforms, to distribute the processing load.

The computer 1900, for example, includes COM ports 1902 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1900 also includes a central processing unit (CPU) 1904, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1906,program storage and data storage of different forms, e.g., disk 1908,read only memory (ROM) 1910, or random access memory (RAM) 1912, forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by the CPU 1904.The computer 1900 also includes an I/O component 1914, supportinginput/output flows between the computer 1900 and other componentstherein such as user interface elements 1916. The computer 1900 may alsoreceive programming and data via network communications.

Hence, aspects of the method of hybrid information query, as outlinedabove, may be embodied in programming. Program aspects of the technologymay be thought of as “products” or “articles of manufacture” typicallyin the form of executable code and/or associated data that is carried onor embodied in a type of machine readable medium. Tangiblenon-transitory “storage” type media include any or all of the memory orother storage for the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide storage at any time for thesoftware programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another. Thus, another typeof media that may bear the software elements includes optical,electrical, and electromagnetic waves, such as used across physicalinterfaces between local devices, through wired and optical landlinenetworks and over various air-links. The physical elements that carrysuch waves, such as wired or wireless links, optical links or the like,also may be considered as media bearing the software. As used herein,unless restricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media can take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer can read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to aprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it can also be implemented as a softwareonly solution. In addition, the components of the system as disclosedherein can be implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

While the foregoing has described what are considered to be the bestmode and/or other examples, it is understood that various modificationsmay be made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that the teachings may beapplied in numerous applications, only some of which have been describedherein. It is intended by the following claims to claim any and allapplications, modifications and variations that fall within the truescope of the present teachings.

We claim:
 1. A method implemented on at least one machine, each of whichhas at least one processor, storage, and a communication platformconnected to a network for hybrid information query, comprising thesteps of: receiving a request from a user associated with a hybridquery, wherein the hybrid query is expressed in accordance with an inputin terms of one of a user, a feature, and a document, and a desiredhybrid query result in terms of one of a user, a feature, and adocument; determining a mapping between the input and the desired hybridquery result; retrieving a hybrid model established based on hybridinformation collected and associated with one or more users, wherein thehybrid model is established by: analyzing the hybrid information toidentify one or more explicit relationships in the hybrid informationand to derive one or more implicit relationships among the one or moreusers based on the hybrid information; and indexing the hybridinformation based on the explicit and implicit relationships withrespect to each of the one or more users; performing the mapping basedon the hybrid model to obtain the desired hybrid query result based onthe input; and providing the desired hybrid query result as a responseto the hybrid query.
 2. The method of claim 1, wherein the hybridinformation collected and associated with one or more users includes atleast one of: static user information about the one or more users;static content information associated with usage of such content by theone or more users; activity information associated with onlineactivities of the one or more users; dynamic user information related toa dynamic online context surrounding the one or more users when they areonline; dynamic content information collected online with respect to theone or more users; derived user information obtained based on at leastone of the static and dynamic user information and the activityinformation of the one or more users; and derived content informationobtained based on at least one of the static and dynamic contentinformation and the activity information of the one or more users. 3.The method of claim 2, wherein the hybrid information is analyzed by:building a static profile based on the static user information andstatic content information for each of the one or more users when theuser is offline; building a dynamic profile based on at least one of thedynamic user information and dynamic content information for each of theone or more users when the user is online; and generating an integratedprofile for each of the one or more users by combing the static profileand the dynamic profile for the user.
 4. The method of claim 2, whereinthe hybrid information is analyzed by: analyzing at least one of thestatic user information and dynamic user information to extractattributes for each of the one or more users; analyzing at least one ofthe static content information and dynamic user information to extracttopics of interest for each of the one or more users; and analyzing theactivity information for the one or more users to infer the implicitrelationships that associate the user with at least one of a topic ofinterest, another user, and content based on the hybrid model.
 5. Themethod of claim 1, wherein the hybrid model is established based on afirst matrix representing user features with respect to the one or moreusers and a second matrix representing topics/keywords with respect tothe one or more users.
 6. The method of claim 1, wherein the mappingincludes at least one of user to user, user to document, user tofeature, document to user, document to document, document to feature,feature to user, feature to document, and feature to feature.
 7. Asystem for hybrid information query, comprising: a hybrid queryinterface configured to: receive a request from a user associated with ahybrid query, wherein the hybrid query is expressed in accordance withan input in terms of one of a user, a feature, and a document, and adesired hybrid query result in terms of one of a user, a feature, and adocument, and provide the desired hybrid query result as a response tothe hybrid query; and a hybrid response recommendation engine configuredto: determine a mapping between the input and the desired hybrid queryresult, retrieve a hybrid model established based on hybrid informationcollected and associated with one or more users, wherein the hybridmodel is established by: analyzing the hybrid information to identifyone or more explicit relationships in the hybrid information and toderive one or more implicit relationships among the one or more usersbased on the hybrid information; and indexing the hybrid informationbased on the explicit and implicit relationships with respect to each ofthe one or more users; and perform the mapping based on the hybrid modelto obtain the desired hybrid query result based on the input.
 8. Thesystem of claim 7, wherein the hybrid information collected andassociated with one or more users includes at least one of: static userinformation about the one or more users; static content informationassociated with usage of such content by the one or more users; activityinformation associated with online activities of the one or more users;dynamic user information related to a dynamic online context surroundingthe one or more users when they are online; dynamic content informationcollected online with respect to the one or more users; derived userinformation obtained based on at least one of the static and dynamicuser information and the activity information of the one or more users;and derived content information obtained based on at least one of thestatic and dynamic content information and the activity information ofthe one or more users.
 9. The system of claim 8, wherein the hybridinformation is analyzed by: building a static profile based on thestatic user information and static content information for each of theone or more users when the user is offline; building a dynamic profilebased on at least one of the dynamic user information and dynamiccontent information for each of the one or more users when the user isonline; and generating an integrated profile for each of the one or moreusers by combing the static profile and the dynamic profile for theuser.
 10. The system of claim 8, wherein the hybrid information isanalyzed by: analyzing at least one of the static user information anddynamic user information to extract attributes for each of the one ormore users; analyzing at least one of the static content information anddynamic user information to extract topics of interest for each of theone or more users; and analyzing the activity information for the one ormore users to infer the implicit relationships that associate the userwith at least one of a topic of interest, another user, and contentbased on the hybrid model.
 11. The system of claim 7, wherein the hybridmodel is established based on a first matrix representing user featureswith respect to the one or more users and a second matrix representingtopics/keywords with respect to the one or more users.
 12. The system ofclaim 7, wherein the mapping includes at least one of user to user, userto document, user to feature, document to user, document to document,document to feature, feature to user, feature to document, and featureto feature.
 13. A machine-readable tangible and non-transitory mediumhaving information for hybrid information query recorded thereon,wherein the information, when read by the machine, causes the machine toperform the following: receiving a request from a user associated with ahybrid query, wherein the hybrid query is expressed in accordance withan input in terms of one of a user, a feature, and a document, and adesired hybrid query result in terms of one of a user, a feature, and adocument; determining a mapping between the input and the desired hybridquery result; retrieving a hybrid model established based on hybridinformation collected and associated with one or more users, wherein thehybrid model is established by: analyzing the hybrid information toidentify one or more explicit relationships in the hybrid informationand to derive one or more implicit relationships among the one or moreusers based on the hybrid information; and indexing the hybridinformation based on the explicit and implicit relationships withrespect to each of the one or more users; performing the mapping basedon the hybrid model to obtain the desired hybrid query result based onthe input; and providing the desired hybrid query result as a responseto the hybrid query.
 14. The medium of claim 13, wherein the hybridinformation collected and associated with one or more users includes atleast one of: static user information about the one or more users;static content information associated with usage of such content by theone or more users; activity information associated with onlineactivities of the one or more users; dynamic user information related toa dynamic online context surrounding the one or more users when they areonline; dynamic content information collected online with respect to theone or more users; derived user information obtained based on at leastone of the static and dynamic user information and the activityinformation of the one or more users; and derived content informationobtained based on at least one of the static and dynamic contentinformation and the activity information of the one or more users. 15.The medium of claim 14, wherein the hybrid information is analyzed by:building a static profile based on the static user information andstatic content information for each of the one or more users when theuser is offline; building a dynamic profile based on at least one of thedynamic user information and dynamic content information for each of theone or more users when the user is online; and generating an integratedprofile for each of the one or more users by combing the static profileand the dynamic profile for the user.
 16. The medium of claim 14,wherein the hybrid information is analyzed by: analyzing at least one ofthe static user information and dynamic user information to extractattributes for each of the one or more users; analyzing at least one ofthe static content information and dynamic user information to extracttopics of interest for each of the one or more users; and analyzing theactivity information for the one or more users to infer the implicitrelationships that associate the user with at least one of a topic ofinterest, another user, and content based on the hybrid model.
 17. Themedium of claim 14, wherein the hybrid model is established based on afirst matrix representing user features with respect to the one or moreusers and a second matrix representing topics/keywords with respect tothe one or more users.
 18. The medium of claim 13, wherein the mappingincludes at least one of user to user, user to document, user tofeature, document to user, document to document, document to feature,feature to user, feature to document, and feature to feature.